Hans Matter

Sanofi Aventis Group, Lutetia Parisorum, Île-de-France, France

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Publications (55)216.1 Total impact

  • [show abstract] [hide abstract]
    ABSTRACT: Drug action can be rationalized as interaction of a molecule with proteins in a regulatory network of targets from a specific biological system. Both drug and side effects are often governed by interaction of the drug molecule with many, often unrelated, targets. Accordingly, arrays of protein-ligand interaction data from numerous in vitro profiling assays today provide growing evidence of polypharmacological drug interactions, even for marketed drugs. In vitro off-target profiling has therefore become an important tool in early drug discovery to learn about potential off-target liabilities, which are sometimes beneficial, but more often safety relevant. The rapidly developing field of in silico profiling approaches is complementing in vitro profiling. These approaches capitalize from large amounts of biochemical data from multiple sources to be exploited for optimizing undesirable side effects in pharmaceutical research. Therefore, current in silico profiling models are nowadays perceived as valuable tools in drug discovery, and promise a platform to support optimally informed decisions.
    Future medicinal chemistry 03/2014; 6(3):295-317. · 3.31 Impact Factor
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    ABSTRACT: We have used a set of four local properties based on semiempirical molecular orbital calculations (the electron density (ρ), hydrogen bond donor field (HDF), hydrogen bond acceptor field (HAF) and molecular lipophilicity potential (MLP)) for 3D-QSAR studies to overcome the limitations of the current force-field based molecular interaction fields (MIFs). These properties can be calculated rapidly and are thus amenable to high-throughput industrial applications. Their statistical performance was compared with that of conventional 3D-QSAR approaches using nine datasets (angiotensin converting enzyme inhibitors (ACE), acetylcholinesterase inhibitors (AchE), benzodiazepine receptor ligands (BZR), cyclooxygenase-2 inhibitors (COX2), dihydrofolate reductase inhibitors (DHFR), glycogen phosphorylase b inhibitors (GPB), thermolysin inhibitors (THER), thrombin inhibitors (THR) and serine protease factor Xa inhibitors (fXa)). The 3D-QSAR models generated were tested thoroughly for robustness and predictive ability. The average performance of the quantum mechanical molecular interaction field (QM-MIF) models for the nine datasets is better than that of the conventional force-field-based MIFs. In the individual datasets, the QM-MIF models always perform better than, or as well as, the conventional approaches. It is particularly encouraging that the relative performance of the QM-MIF models improves in the external validation. In addition, the models generated showed statistical stability with respect to model building procedure variations such as grid spacing size and grid orientation. QM-MIF contour maps reproduce the features important for ligand binding for the example dataset (factor Xa inhibitors), demonstrating the intuitive chemical interpretability of QM-MIFs.
    Journal of Chemical Information and Modeling 05/2013; · 4.30 Impact Factor
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    ABSTRACT: A novel procedure for in-silico rescaffolding and side chain optimization is reported with explicit consideration of the route of chemical synthesis and availability of compatible chemical reagents. We have defined a set of retrosynthetic disconnections representing robust chemical reactions, amenable for combinatorial chemistry. This rule set is used to generate virtual fragment databases from available chemical reagents. Each reactive center is annotated with its compatibility with regard to the chemical reactions. The rule set is then applied to a new molecule to obtain separate query subunits for rescaffolding by 3D shape matching in specific reagent-derived fragment databases. Thus, only fragment hits compatible with the chemistry and shape of the corresponding query moiety are investigated further. The identified fragment hits directly indicate (1) available chemical reagents that can replace the query moiety in the starting molecule and (2) the route for the synthesis of the proposed molecules.
    Journal of Medicinal Chemistry 04/2013; · 5.61 Impact Factor
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    ABSTRACT: Stearoyl-CoA desaturase (SCD1) is linked to the pathogenesis of obesity, dyslipidemia and type 2 diabetes. It is the rate-limiting enzyme in the synthesis of monounsaturated 16:1 n-7 and 18:1 n-9 fatty acyl-CoAs and catalyses an essential part of lipogenesis. Here, we describe the identification, in vitro properties and in vivo efficacy of a novel class of heterocyclic small molecule hexahydro-pyrrolopyrrole SCD1 inhibitors. SAR707, a compound representative for the series, was optimised to high in vitro potency, selectivity and favourable overall properties in enzymatic and cellular assays. In vivo, this compound reduced serum desaturation index, decreased body weight gain and improved lipid parameters and blood glucose levels of obese Zucker diabetic fatty rats treated for 4 weeks in a chronic study. In parallel, fissures of the eye lid, alopecia and inflammation of the skin were observed from day 11 on in all animals treated with the same metabolically active dose. In summary, we described in vitro and in vivo properties of a novel, potent and selective SCD1 inhibitor that improved body weight, blood glucose and triglycerides in an animal model of obesity, type 2 diabetes and dyslipidemia. However, the favourable in vivo properties of systemic SCD1 inhibition shown in our study were accompanied by dose-dependently occurring adverse target-related effects observed in skin. Thus, systemic SCD1 inhibition by small molecules might therefore not represent a feasible approach for the treatment of chronic metabolic diseases.
    European journal of pharmacology 03/2013; · 2.59 Impact Factor
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    ABSTRACT: The discovery of potent benzimidazole stearoyl-CoA desaturase (SCD1) inhibitors by ligand-based virtual screening is described. ROCS 3D-searching gave a favorable chemical motif that was subsequently optimized to arrive at a chemical series of potent and promising SCD1 inhibitors. In particular, compound SAR224 was selected for further pharmacological profiling based on favorable in vitro data. After oral administration to male ZDF rats, this compound significantly decreased the serum fatty acid desaturation index, thus providing conclusive evidence for SCD1 inhibition in vivo by SAR224.
    Bioorganic & medicinal chemistry letters 01/2013; · 2.65 Impact Factor
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    ABSTRACT: Current 3D-QSAR methods such as CoMFA or CoMSIA make use of classical force-field approaches for calculating molecular fields. Thus, they can not adequately account for noncovalent interactions involving halogen atoms like halogen bonds or halogen-π interactions. These deficiencies in the underlying force fields result from the lack of treatment of the anisotropy of the electron density distribution of those atoms, known as the "σ-hole", although recent developments have begun to take specific interactions such as halogen bonding into account. We have now replaced classical force field derived molecular fields by local properties such as the local ionization energy, local electron affinity, or local polarizability, calculated using quantum-mechanical (QM) techniques that do not suffer from the above limitation for 3D-QSAR. We first investigate the characteristics of QM-based local property fields to show that they are suitable for statistical analyses after suitable pretreatment. We then analyze these property fields with partial least-squares (PLS) regression to predict biological affinities of two data sets comprising factor Xa and GABA-A/benzodiazepine receptor ligands. While the resulting models perform equally well or even slightly better in terms of consistency and predictivity than the classical CoMFA fields, the most important aspect of these augmented field-types is that the chemical interpretation of resulting QM-based property field models reveals unique SAR trends driven by electrostatic and polarizability effects, which cannot be extracted directly from CoMFA electrostatic maps. Within the factor Xa set, the interaction of chlorine and bromine atoms with a tyrosine side chain in the protease S1 pocket are correctly predicted. Within the GABA-A/benzodiazepine ligand data set, PLS models of high predictivity resulted for our QM-based property fields, providing novel insights into key features of the SAR for two receptor subtypes and cross-receptor selectivity of the ligands. The detailed interpretation of regression models derived using improved QM-derived property fields thus provides a significant advantage by revealing chemically meaningful correlations with biological activity and helps in understanding novel structure-activity relationship features. This will allow such knowledge to be used to design novel molecules on the basis of interactions additional to steric and hydrogen-bonding features.
    Journal of Chemical Information and Modeling 08/2012; 52(9):2441-53. · 4.30 Impact Factor
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    ABSTRACT: The pregnane X receptor (PXR), a member of the nuclear hormone superfamily, regulates the expression of several enzymes and transporters involved in metabolically relevant processes. The significant induction of CYP450 enzymes by PXR, in particular CYP3A4, might significantly alter the metabolism of prescribed drugs. In order to early identify molecules in drug discovery with a potential to activate PXR as antitarget, we developed fast and reliable in silico filters by ligand-based QSAR techniques. Two classification models were established on a diverse dataset of 434 drug-like molecules. A second augmented set allowed focusing on interesting regions in chemical space. These classifiers are based on decision trees combined with a genetic algorithm based variable selection to arrive at predictive models. The classifier for the first dataset on 29 descriptors showed good performance on a test set with a correct classification of both 100% for PXR activators and non-activators plus 87% for activators and 83% for non-activators in an external dataset. The second classifier then correctly predicts 97% activators and 91% non-activators in a test set and 94% for activators and 64% non-activators in an external set of 50 molecules, which still qualifies for application as a filter focusing on PXR activators. Finally a quantitative model for PXR activation for a subset of these molecules was derived using a regression-tree approach combined with GA variable selection. This final model shows a predictive r(2) of 0.774 for the test set and 0.452 for an external set of 33 molecules. Thus, the combination of these filters consistently provide guidelines for lowering PXR activation in novel candidate molecules.
    Bioorganic & medicinal chemistry 04/2012; 20(18):5352-65. · 2.82 Impact Factor
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    ABSTRACT: The superadditivity of fragment linking on affinities was quantified by systematic deconstruction of a fXa inhibitor. In their Communication (DOI: 10.1002/anie.201107091), M. Nazaré, H. Matter, and co-workers show that by connecting two fragments with a single bond, a linker contribution of -14.0 kJ mol(-1) results, which corresponds to an affinity improvement of about 2.5 orders of magnitude relative to the sum of fragment affinities.
    Angewandte Chemie International Edition 12/2011; · 13.73 Impact Factor
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    ABSTRACT: More than just the sum of its parts: The superadditivity effect of fragment linking on ΔG was quantified by deconstructing two fXa inhibitors with congeneric fragments, but different linkers. By connecting both fragments with a single bond, a high linker contribution ΔG(link) of -14.0 kJ mol(-1) results, which corresponds to an improvement in affinity by around 2.5 orders of magnitude relative to the sum of fragment ΔG values.
    Angewandte Chemie International Edition 12/2011; 51(4):905-11. · 13.73 Impact Factor
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    ABSTRACT: The selective inhibition of the aspartyl protease renin is of high interest to control hypertension and associated cardiovascular risk factors. Following on preceding contributions, we report herein on the optimization of two series of azaindoles to arrive at potent and non-chiral renin inhibitors. The previously discovered azaindole scaffold was further explored by structure-based drug design in combination with parallel synthesis. This results in the identification of novel 5- or 7-azaindole derivatives with remarkable potency for renin inhibition. The best compounds on both series show IC(50) values between 3 and 8nM.
    Bioorganic & medicinal chemistry letters 09/2011; 21(18):5487-92. · 2.65 Impact Factor
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    ABSTRACT: The control of hypertension and associated cardiovascular risk factors is possible by selective inhibition of the aspartyl protease renin due to its unique position in the renin-angiotensin system. Starting from a previously disclosed series of potent and nonchiral indole-3-carboxamides, we further explored this motif by structure-based drug design guided by X-ray crystallography in combination with efficient parallel synthesis. This resulted in the discovery of 4- or 6-azaindole derivatives with remarkable potency for renin inhibition. The best compound from these series showed an IC(50) value of 1.3 nM.
    Bioorganic & medicinal chemistry letters 09/2011; 21(18):5480-6. · 2.65 Impact Factor
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    ABSTRACT: Compounds that simultaneously activate the peroxisome proliferator-activated receptor (PPAR) subtypes PPARγ and PPARδ have the potential to effectively target dyslipidemia and type II diabetes in a single pharmaceutically active molecule. The frequently observed side effects of selective PPARγ agonists, such as edema and weight gain, are expected to be overcome by using partial instead of full agonists for this nuclear receptor family. Herein we report the discovery, synthesis, and optimization of a novel series of sulfonylthiadiazoles that are active as partial agonists. The initial compound 6 was discovered by high-throughput screening as a moderate partial PPARδ agonist; its optimization was based on the X-ray crystal structure in complex with PPARδ. In contrast to other PPARδ agonists, this ligand does not interact directly with residues from the activation helix AF-2, which might be linked to its partial agonistic effect. Interestingly, the thiadiazole moiety fills a novel subpocket, which becomes accessible after moderate conformational rearrangement. The optimization was focused on introducing conformational constraints and replacing intramolecular hydrogen bonding interactions. Highly potent molecules with activity as dual partial PPARγ/δ agonists in the low nanomolar range were then identified. One of the most active members, compound 20 a, displayed EC₅₀ values of 1.6 and 336 nM for PPARδ and γ, respectively. The X-ray crystal structure of its complex with PPARδ confirms our design hypothesis. Compound 20 a clearly displayed in vivo activity in two chronic mice studies. Lipids were modified in a beneficial way in normolipidemic mice, and the development of overt diabetes could be prevented in pre-diabetic db/db mice. However, body weight gain was similar to that observed with the PPARγ agonist rosiglitazone. Hence, active compounds from this series can be considered as valuable tools to elucidate the complex roles of dual PPARγ/δ agonists for potential treatment of metabolic syndrome.
    ChemMedChem 03/2011; 6(4):633-53. · 2.84 Impact Factor
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    ABSTRACT: Selective inhibition of the aspartyl protease renin has gained attraction as an interesting approach to control hypertension and associated cardiovascular risk factors given its unique position in the renin-angiotensin system. Using a combination of high-throughput screening, parallel synthesis, X-ray crystallography and structure-based design, we identified and optimized a novel series of potent and non-chiral indole-3-carboxamides with remarkable potency for renin. The most potent compound 5k displays an IC(50) value of 2nM.
    Bioorganic & medicinal chemistry letters 11/2010; 20(21):6268-72. · 2.65 Impact Factor
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    ABSTRACT: Attractive chlorine: Noncovalent interactions between chlorine or bromine atoms and aromatic rings in proteins open up a new method for the manipulation of molecular recognition. Substitution at distinct positions of two factor Xa inhibitors improves the free energy of binding by interaction with a tyrosine unit. The generality of this motif was underscored by multiple crystal structures as well as high-level quantum chemical calculations (see picture).
    Angewandte Chemie International Edition 02/2009; 48(16):2911-6. · 13.73 Impact Factor
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    ABSTRACT: Empirical scoring functions to calculate binding affinities of protein-ligand complexes have been calibrated based on experimental structure and affinity data collected from public and industrial sources. Public data were taken from the AffinDB database, whereas access to industrial data was gained through the Scoring Function Consortium (SFC), a collaborative effort with various pharmaceutical companies and the Cambridge Crystallographic Data Center. More than 850 complexes were obtained by the data collection procedure and subsequently used to setup different training sets for the parameterization of new scoring functions. Over 60 different descriptors were evaluated for all complexes, including terms accounting for interactions with and among aromatic ring systems as well as many surface-dependent terms. After exploratory correlation and regression analyses, stepwise variable selection procedures and systematic searches, the most suitable descriptors were chosen as variables to calibrate regression functions by means of multiple linear regression or partial least squares analysis. Eight different functions are presented herein. Cross-validated r(2) (Q(2)) values of up to 0.72 and standard errors (s(PRESS)) generally below 1.15 pK(i) units suggest highly predictive functions. Extensive unbiased validation was carried out by testing the functions on large data sets from the PDBbind database as used by Wang et al. (J Chem Inf Comput Sci 2004;44:2114-2125) in a comparative analysis of other scoring functions. Superior performance of the SFCscore functions is observed in many cases, but the results also illustrate the need for further improvements.
    Proteins Structure Function and Bioinformatics 05/2008; 73(2):395-419. · 3.34 Impact Factor
  • Hans Matter, Matthias Rarey
    12/2007: pages 409 - 439; , ISBN: 9783527613502
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    ABSTRACT: Cytochrome P450 2C9 (CYP2C9) is one of the most important phase 1 metabolizing enzymes in humans for therapeutically relevant pharmaceuticals. Any new compound inhibiting this membrane-associated protein would notably affect the metabolism of physiologically important molecules and drugs, resulting in clinically significant drug-drug interactions. In search for computational tools to identify potential CYP2C9 inhibitors early in discovery, we present here the construction of filters based on 1100 structurally diverse molecules tested for CYP2C9 inhibition under identical conditions. Their chemical structures were encoded using various 2D descriptors including Three-Point Pharmacophoric (3PP) fingerprints, followed by generation of statistical models using Support Vector Machines (SVM) and Partial Least Squares (PLS). This consistently led to significant and predictive models for regression and classification of CYP2C9 inhibitors. Their predictive ability was underscored by successfully applying them to different test sets of 238 diverse and 344 GPCR-targeted compounds. Even more important for early drug discovery is the ability of these models to correctly discriminate CYP2C9 inhibitors from inactive molecules. These models collectively are able to identify true CYP2C9 inhibitors with relatively low rates of false positives. The 3PP-based filter also allows visualizing important substructures and functional groups, which are linked to protein-ligand interactions for CYP2C9, as illustrated for selected structure-activity series. The application of these models to the substrate S-warfarin, recently co-crystallized with CYP2C9, revealed that the important substructures are indeed involved in the interaction with the CYP2C9 binding site. For example, the model correctly indicated aromatic stacking interactions with Phe114 and Phe476 as well as a hydrogen bond with Phe100. Hence, these models consistently provide guidelines for reducing CYP2C9 inhibition in novel candidate molecules.
    QSAR & Combinatorial Science 12/2006; 26(5):618 - 628. · 1.55 Impact Factor
  • Bernard Pirard, Hans Matter
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    ABSTRACT: To gain insight into the structural determinants for the matrix metalloproteinase (MMP) family, we characterized the binding sites of 56 MMP structures and one TACE (tumor necrosis factor alpha converting enzyme) structure using molecular interaction fields (MIFs). These MIFs were produced by two approaches: the GRID force field and the knowledge-based potential DrugScore. The subsequent statistical analysis using consensus principal component analysis (CPCA) for the entire binding site and each subpockets revealed both approaches to encode similar information about discriminating regions. However, the relative importance of the probes varied between both approaches. The CPCA models provided the following ranking of the six subpockets based on the opportunity for selective interactions with different MMPs: S1' > S2, S3, S3' > S1, S2'. The interpretation of these models agreed with experimental binding modes inferred from crystal structures or docking.
    Journal of Medicinal Chemistry 01/2006; 49(1):51-69. · 5.61 Impact Factor
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    ABSTRACT: We present a novel approach for ligand-based virtual screening by combining query molecules into a multiple feature tree model called MTree. All molecules are described by the established feature tree descriptor, which is derived from a topological molecular graph. A new pairwise alignment algorithm leads to a consistent topological molecular alignment based on chemically reasonable matching of corresponding functional groups. These multiple feature tree models find application in ligand-based virtual screening to identify new lead structures for chemical optimization. Retrospective virtual screening with MTree models generated for angiotensin-converting enzyme and the alpha1a receptor on a large candidate database yielded enrichment factors up to 71 for the first 1% of the screened database. MTree models outperformed database searches using single feature trees in terms of hit rates and quality and additionally identified alternative molecular scaffolds not included in any of the query molecules. Furthermore, relevant molecular features, which are known to be important for affinity to the target, are identified by this new methodology.
    Journal of Medicinal Chemistry 11/2005; 48(21):6575-84. · 5.61 Impact Factor
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    ABSTRACT: In this paper, we compare protein- and ligand-based virtual screening techniques for identifying the ligands of four biogenic amine-binding G-protein coupled receptors (GPCRs). For the screening of the virtual compound libraries, we used (1) molecular docking into GPCR homology models, (2) ligand-based pharmacophore and Feature Tree models, (3) three-dimensional (3D)-similarity searches, and (4) statistical methods [partial least squares (PLS) and partial least squares discriminant analysis (PLS-DA) models] based on two-dimensional (2D) molecular descriptors. The comparison of the different methods in retrieving known antagonists from virtual libraries shows that in our study the ligand-based pharmacophore-, Feature Tree-, and 2D quantitative structure-activity relationship (QSAR)-based screening techniques provide enrichment factors that are higher than those provided by molecular docking into the GPCR homology models. Nevertheless, the hit rates achieved when docking with GOLD and ranking the ligands with GoldScore (up to 60% among the top-ranked 1% of the screened databases) are still satisfying. These results suggest that docking into GPCR homology models can be a useful approach for lead finding by virtual screening when either little or no information about the active ligands is available.
    Journal of Medicinal Chemistry 09/2005; 48(17):5448-65. · 5.61 Impact Factor

Publication Stats

590 Citations
216.10 Total Impact Points


  • 2013
    • Sanofi Aventis Group
      Lutetia Parisorum, Île-de-France, France
  • 2007
    • University of Tuebingen
      • Institute of Inorganic Chemistry
      Tübingen, Baden-Wuerttemberg, Germany
  • 2006
    • Novartis
      Berna, Bern, Switzerland
  • 2005
    • Heinrich-Heine-Universität Düsseldorf
      Düsseldorf, North Rhine-Westphalia, Germany
  • 2004
    • University of New Mexico
      Albuquerque, New Mexico, United States